Construction of multiple-rate QC-LDPC codes using hierarchical row-splitting

In this letter, we propose an improved method called hierarchical row-splitting with edge variation for designing multiple-rate quasi-cyclic low-density parity-check (QC-LDPC) codes, which constructs lower-rate codes from a high-rate mother code by row-splitting operations. Consequently, the obtained QC-LDPC codes with various code rates have the same blocklength and can share common hardware resources to reduce the implementation complexity. Compared with the conventional row-combining-based algorithms, a wider range of code rates are supported. Moreover, each individual rate code could be separately optimized, making it easier to find a set of multiple-rate QC-LDPC codes with good performance for all different rates. Simulation results demonstrate that the obtained codes outperform the counterparts from digital video broadcasting-second generation terrestrial

Reason and Guarantee Measures for Construction Cracks of Pavement Concrete in High Temperature Period

The crack problem of concrete pavement is the key to ensure road safety and long-term service. The special environment of high temperature in summer is easy to lead to concrete cracking, this paper analyzes the reason of concrete cracking according to the situation, and puts forward the maintenance measures of concrete pavement under high temperature from the aspects of raw material quality management, concrete mixing, transportation, pouring and so on, which can provide reference for the quality control of concrete road under the high temperature

Location-aware channel estimation enhanced TDD based massive MIMO

Pilot contamination (PC) is a stumbling block in of realizing massive multi-input multi-output (MIMO) systems. This contribution proposes a location-aware channel estimation-enhanced massive MIMO system employing time-division duplexing protocol, which is capable of significantly reducing the inter-cell interference caused by PC and, therefore, improving the achievable system performance. Specifically, we present a novel location-aware channel estimation algorithm, which utilizes the property of the steering vector to carry out a fast Fourier transform-based post-processing after the conventional pilot-aided channel estimation for mitigating PC. Our asymptotic analysis proves that this post-processing is capable of removing PC from the interfering users with different angle-of-arrivals (AOAs). Since in practice the AOAs of some users may be similar, we further present a location-aware pilot assignment method to ensure that users utilizing the same pilot have distinguishable AOAs, in order to fully benefit from the location-aware channel estimation. Simulation results demonstrate that the proposed scheme can dramatically reduce the inter-cell interference caused by the re-use of the pilot sequence and improve the overall system performance significantly, while only imposing a modest extra computational cost, in comparison with the conventional pilot-aided channel estimation

Equivariant Energy-Guided SDE for Inverse Molecular Design

Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties. In this paper, we propose equivariant energy-guided stochastic differential equations (EEGSDE), a flexible framework for controllable 3D molecule generation under the guidance of an energy function in diffusion models. Formally, we show that EEGSDE naturally exploits the geometric symmetry in 3D molecular conformation, as long as the energy function is invariant to orthogonal transformations. Empirically, under the guidance of designed energy functions, EEGSDE significantly improves the baseline on QM9, in inverse molecular design targeted to quantum properties and molecular structures. Furthermore, EEGSDE is able to generate molecules with multiple target properties by combining the corresponding energy functions linearly

Coarse-to-Fine Contrastive Learning on Graphs

Inspired by the impressive success of contrastive learning (CL), a variety of graph augmentation strategies have been employed to learn node representations in a self-supervised manner. Existing methods construct the contrastive samples by adding perturbations to the graph structure or node attributes. Although impressive results are achieved, it is rather blind to the wealth of prior information assumed: with the increase of the perturbation degree applied on the original graph, 1) the similarity between the original graph and the generated augmented graph gradually decreases; 2) the discrimination between all nodes within each augmented view gradually increases. In this paper, we argue that both such prior information can be incorporated (differently) into the contrastive learning paradigm following our general ranking framework. In particular, we first interpret CL as a special case of learning to rank (L2R), which inspires us to leverage the ranking order among positive augmented views. Meanwhile, we introduce a self-ranking paradigm to ensure that the discriminative information among different nodes can be maintained and also be less altered to the perturbations of different degrees. Experiment results on various benchmark datasets verify the effectiveness of our algorithm compared with the supervised and unsupervised models

Hypermethylated gene ANKDD1A is a candidate tumor suppressor that interacts with FIH1 and decreases HIF1α stability to inhibit cell autophagy in the glioblastoma multiforme hypoxia microenvironment.

Ectopic epigenetic mechanisms play important roles in facilitating tumorigenesis. Here, we first demonstrated that ANKDD1A is a functional tumor suppressor gene, especially in the hypoxia microenvironment. ANKDD1A directly interacts with FIH1 and inhibits the transcriptional activity of HIF1α by upregulating FIH1. In addition, ANKDD1A decreases the half-life of HIF1α by upregulating FIH1, decreases glucose uptake and lactate production, inhibits glioblastoma multiforme (GBM) autophagy, and induces apoptosis in GBM cells under hypoxia. Moreover, ANKDD1A is highly frequently methylated in GBM. The tumor-specific methylation of ANKDD1A indicates that it could be used as a potential epigenetic biomarker as well as a possible therapeutic target